1. A decision support system based on Bayesian modelling for pest management: Application to wireworm risk assessment in maize fields
- Author
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Julien Roche, Manuel Plantegenest, Philippe Larroudé, Jean-Baptiste Thibord, Le Cointe Ronan, Sylvain Poggi, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Rennes Angers, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), ARVALIS - Institut du végétal [Paris], This study was funded by SEMAE (formerly GNIS) as part of the TAUPINLAND project. The data was previously collected as part of the TAUPIN project (CASDAR, project no. 1133) supported by the French Ministry of Agriculture., and Elsevier
- Subjects
Wireworm control ,[STAT]Statistics [stat] ,Crop protection ,Pest risk assessment ,[SDV]Life Sciences [q-bio] ,Hierarchical Bayesian model ,Integrated pest management (IPM) ,Decision support system - Abstract
International audience; Protecting crops against pests is a major issue in the current agricultural production system. In particular,assessing the risk to crops can promote integrated pest management (IPM) strategies that encourage naturalcontrol mechanisms and advocate the use of pesticides as a last resort. In this study, we focused on wireworms,major soil-dwelling insect pests inflicting severe economic damage on various crops (including maize, potatoesand cereals) across Europe and North America. We have developed an original hierarchical Bayesian model thatexplicitly accounts for biological knowledge and uncertainty in field observations, rather than relying solely onstatistical correlations, to predict the level of wireworm infestation. The model was calibrated and validatedusing a substantial dataset originating from an agro-environmental survey carried out over three consecutiveyears (2012–2014) in France, which provides the wireworm abundance in 419 maize fields, together with informationon the landscape context, field history, weather conditions, soil characteristics and farming practicesassociated to each field. Model outcomes show good agreement with current knowledge from literature and fieldexpertise in terms of the effects of variables on wireworm abundance, and provide fairly good predictive capacity.Subsequently, the model was encapsulated as a software (R shiny application) to predict the risk ofwireworm infestation in any field of interest, and can be used by farmers or agricultural advisors as a decisionsupport system for the implementation of IPM strategies. The conceptual framework that we implemented can beadapted to a wide range of similar situations involving other crops and pests.
- Published
- 2023